Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

Numeric10
Categorical1

Warnings

target is uniformly distributed Uniform
X0 has unique values Unique
X1 has unique values Unique
X2 has unique values Unique
X3 has unique values Unique
X4 has unique values Unique
X5 has unique values Unique
X6 has unique values Unique
X7 has unique values Unique
X8 has unique values Unique
X9 has unique values Unique

Reproduction

Analysis started2021-02-12 17:41:44.216269
Analysis finished2021-02-12 17:54:47.102059
Duration13 minutes and 2.89 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

X0
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01302329029
Minimum-3.631843041
Maximum2.852008687
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:54:55.209870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.631843041
5-th percentile-1.532394622
Q1-0.5959727691
median-0.0002580799964
Q30.6364685765
95-th percentile1.56922434
Maximum2.852008687
Range6.483851728
Interquartile range (IQR)1.232441346

Descriptive statistics

Standard deviation0.9346541394
Coefficient of variation (CV)71.7678957
Kurtosis0.005393463112
Mean0.01302329029
Median Absolute Deviation (MAD)0.616744737
Skewness-0.04993779675
Sum13.02329029
Variance0.8735783603
MonotocityNot monotonic
2021-02-12T12:55:03.136122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.35199802841
 
0.1%
-0.45801533891
 
0.1%
1.0753258341
 
0.1%
-0.11725334681
 
0.1%
-1.0671629131
 
0.1%
-0.29721423231
 
0.1%
-1.0627530851
 
0.1%
-0.36886627161
 
0.1%
1.5391499631
 
0.1%
0.40372209081
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.6318430411
0.1%
-2.8686272881
0.1%
-2.5840970691
0.1%
-2.5735007211
0.1%
-2.448045341
0.1%
-2.4303562131
0.1%
-2.4124320161
0.1%
-2.359280211
0.1%
-2.3546497721
0.1%
-2.265184081
0.1%
ValueCountFrequency (%)
2.8520086871
0.1%
2.6811563541
0.1%
2.4311789931
0.1%
2.3844103071
0.1%
2.3334140121
0.1%
2.2284272461
0.1%
2.1989107651
0.1%
2.1618464221
0.1%
2.1427178411
0.1%
2.0853574751
0.1%

X1
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03103280395
Minimum-3.20065569
Maximum2.695667681
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:55:10.886305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.20065569
5-th percentile-1.622883297
Q1-0.7028527322
median-0.04546756748
Q30.5890496359
95-th percentile1.68862179
Maximum2.695667681
Range5.896323371
Interquartile range (IQR)1.291902368

Descriptive statistics

Standard deviation0.9896478538
Coefficient of variation (CV)-31.89037818
Kurtosis-0.08205285573
Mean-0.03103280395
Median Absolute Deviation (MAD)0.6529433684
Skewness0.06485107079
Sum-31.03280395
Variance0.9794028745
MonotocityNot monotonic
2021-02-12T12:55:19.935858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.60052451231
 
0.1%
-1.5972895281
 
0.1%
0.68465219611
 
0.1%
0.88877139721
 
0.1%
-1.7942127761
 
0.1%
-0.15571254231
 
0.1%
-1.0179068081
 
0.1%
-0.93104257121
 
0.1%
-0.19842530751
 
0.1%
-0.7942069831
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.200655691
0.1%
-2.9809156081
0.1%
-2.6805283461
0.1%
-2.627691671
0.1%
-2.5564898411
0.1%
-2.5097168941
0.1%
-2.5076139511
0.1%
-2.4872421
0.1%
-2.2722654041
0.1%
-2.2649473521
0.1%
ValueCountFrequency (%)
2.6956676811
0.1%
2.6350664791
0.1%
2.5795575681
0.1%
2.5179385121
0.1%
2.5021799841
0.1%
2.500544941
0.1%
2.4601553111
0.1%
2.3570615561
0.1%
2.3440407671
0.1%
2.3434174081
0.1%

X2
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007959027731
Minimum-3.637035429
Maximum3.40258579
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:55:28.485582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.637035429
5-th percentile-1.59713371
Q1-0.688052665
median-0.01854483064
Q30.7303474226
95-th percentile1.677337023
Maximum3.40258579
Range7.039621219
Interquartile range (IQR)1.418400088

Descriptive statistics

Standard deviation1.024952386
Coefficient of variation (CV)128.7785922
Kurtosis0.04520469895
Mean0.007959027731
Median Absolute Deviation (MAD)0.6994134833
Skewness-0.04605411476
Sum7.959027731
Variance1.050527394
MonotocityNot monotonic
2021-02-12T12:55:37.693771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.92295770641
 
0.1%
1.0031484741
 
0.1%
-0.60457910691
 
0.1%
0.33049293721
 
0.1%
0.84084571191
 
0.1%
1.056163151
 
0.1%
1.0421854551
 
0.1%
-0.027271416541
 
0.1%
0.24662421771
 
0.1%
1.294067611
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.6370354291
0.1%
-3.2637743351
0.1%
-3.0210541591
0.1%
-2.9586361951
0.1%
-2.9510227371
0.1%
-2.9138946971
0.1%
-2.5889647561
0.1%
-2.486093711
0.1%
-2.4442605451
0.1%
-2.3838305281
0.1%
ValueCountFrequency (%)
3.402585791
0.1%
3.1594170691
0.1%
2.6684761051
0.1%
2.5877412251
0.1%
2.572782051
0.1%
2.5015432881
0.1%
2.4422443731
0.1%
2.3272237751
0.1%
2.3218509031
0.1%
2.297343071
0.1%

X3
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03906491305
Minimum-3.463059678
Maximum3.275869562
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:55:46.491125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.463059678
5-th percentile-1.658034862
Q1-0.6528532639
median0.03372900966
Q30.7599309816
95-th percentile1.674031985
Maximum3.275869562
Range6.73892924
Interquartile range (IQR)1.412784245

Descriptive statistics

Standard deviation1.019061174
Coefficient of variation (CV)26.0863546
Kurtosis-0.03971200728
Mean0.03906491305
Median Absolute Deviation (MAD)0.7109861905
Skewness-0.06869344522
Sum39.06491305
Variance1.038485677
MonotocityNot monotonic
2021-02-12T12:55:54.676331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.21024795461
 
0.1%
0.60665030741
 
0.1%
-0.47315293011
 
0.1%
-0.37721517041
 
0.1%
0.03134598811
 
0.1%
-1.1768831951
 
0.1%
1.6434804291
 
0.1%
-0.2635458561
 
0.1%
-1.3522786951
 
0.1%
-0.47208022511
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.4630596781
0.1%
-3.2144470441
0.1%
-2.9680341371
0.1%
-2.794755281
0.1%
-2.6888318151
0.1%
-2.6577704781
0.1%
-2.6564951411
0.1%
-2.4939208711
0.1%
-2.4420671941
0.1%
-2.4170226721
0.1%
ValueCountFrequency (%)
3.2758695621
0.1%
3.175889471
0.1%
2.5525976581
0.1%
2.486273151
0.1%
2.4518772581
0.1%
2.4373025411
0.1%
2.393494881
0.1%
2.3868182491
0.1%
2.3823785231
0.1%
2.3557431961
0.1%

X4
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03485353761
Minimum-3.385910044
Maximum3.15723554
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:56:02.946518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.385910044
5-th percentile-1.561627235
Q1-0.63027607
median0.02690495465
Q30.704292628
95-th percentile1.567013587
Maximum3.15723554
Range6.543145584
Interquartile range (IQR)1.334568698

Descriptive statistics

Standard deviation0.9780669677
Coefficient of variation (CV)28.06220071
Kurtosis0.06050870023
Mean0.03485353761
Median Absolute Deviation (MAD)0.6711229216
Skewness0.007410284687
Sum34.85353761
Variance0.9566149932
MonotocityNot monotonic
2021-02-12T12:56:11.229625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.89463981091
 
0.1%
0.18719999571
 
0.1%
-1.3003587821
 
0.1%
0.5111053661
 
0.1%
-1.5433705091
 
0.1%
0.95706049991
 
0.1%
-1.1449137881
 
0.1%
0.074891180631
 
0.1%
-0.017576578221
 
0.1%
-0.11067648411
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.3859100441
0.1%
-2.941271911
0.1%
-2.920753631
0.1%
-2.7346073411
0.1%
-2.6936084211
0.1%
-2.4831332111
0.1%
-2.4524463721
0.1%
-2.4321481571
0.1%
-2.385494111
0.1%
-2.3290341831
0.1%
ValueCountFrequency (%)
3.157235541
0.1%
3.1261496751
0.1%
2.8935464841
0.1%
2.7022836471
0.1%
2.6470700731
0.1%
2.5050887861
0.1%
2.3856774391
0.1%
2.3240649231
0.1%
2.3177046061
0.1%
2.3033235821
0.1%

X5
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02059679538
Minimum-2.992178955
Maximum3.058573529
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:56:19.382377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.992178955
5-th percentile-1.651512669
Q1-0.6819863249
median0.02660708171
Q30.6782313892
95-th percentile1.618108069
Maximum3.058573529
Range6.050752484
Interquartile range (IQR)1.360217714

Descriptive statistics

Standard deviation0.9937902899
Coefficient of variation (CV)48.249753
Kurtosis-0.1993771245
Mean0.02059679538
Median Absolute Deviation (MAD)0.670096956
Skewness-0.009415201588
Sum20.59679538
Variance0.9876191403
MonotocityNot monotonic
2021-02-12T12:56:28.062032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.081278967471
 
0.1%
0.66316293411
 
0.1%
0.45676326471
 
0.1%
0.21696257011
 
0.1%
0.14985421391
 
0.1%
-0.85140939721
 
0.1%
-0.74799458591
 
0.1%
1.2865991551
 
0.1%
0.35276693541
 
0.1%
-0.66374657791
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.9921789551
0.1%
-2.7528160281
0.1%
-2.6430673141
0.1%
-2.5649654961
0.1%
-2.4296785471
0.1%
-2.4161052681
0.1%
-2.4136410251
0.1%
-2.2778059111
0.1%
-2.1963318821
0.1%
-2.1944975641
0.1%
ValueCountFrequency (%)
3.0585735291
0.1%
2.7250097161
0.1%
2.6665543961
0.1%
2.6360629071
0.1%
2.5404809741
0.1%
2.4633761051
0.1%
2.4445211851
0.1%
2.3195138831
0.1%
2.3044848441
0.1%
2.2823470411
0.1%

X6
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.006533374389
Minimum-4.014143566
Maximum3.100859106
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:56:36.698725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-4.014143566
5-th percentile-1.748007079
Q1-0.6638295246
median-0.005437939271
Q30.6981052296
95-th percentile1.550204307
Maximum3.100859106
Range7.115002672
Interquartile range (IQR)1.361934754

Descriptive statistics

Standard deviation0.9894190805
Coefficient of variation (CV)-151.4407443
Kurtosis0.06143985736
Mean-0.006533374389
Median Absolute Deviation (MAD)0.6837177211
Skewness-0.1735886404
Sum-6.533374389
Variance0.9789501168
MonotocityNot monotonic
2021-02-12T12:56:44.773405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.084815683421
 
0.1%
1.1216389881
 
0.1%
-1.3257334091
 
0.1%
0.32713971231
 
0.1%
0.87720355321
 
0.1%
0.1495001221
 
0.1%
1.4159176751
 
0.1%
-1.0793269791
 
0.1%
0.84114581811
 
0.1%
0.4296664841
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-4.0141435661
0.1%
-3.3851118731
0.1%
-2.6852765891
0.1%
-2.6198514561
0.1%
-2.5305529091
0.1%
-2.4599756871
0.1%
-2.4531555311
0.1%
-2.4502027311
0.1%
-2.422965841
0.1%
-2.3991104991
0.1%
ValueCountFrequency (%)
3.1008591061
0.1%
2.5187297711
0.1%
2.4579857411
0.1%
2.4321791241
0.1%
2.4134919241
0.1%
2.407901761
0.1%
2.336955541
0.1%
2.3173788421
0.1%
2.2986661971
0.1%
2.2241654291
0.1%

X7
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02215268596
Minimum-3.151923051
Maximum3.083577747
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:56:53.123252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.151923051
5-th percentile-1.641401524
Q1-0.6809244698
median0.03834038294
Q30.7144804838
95-th percentile1.599391712
Maximum3.083577747
Range6.235500798
Interquartile range (IQR)1.395404954

Descriptive statistics

Standard deviation0.9866056282
Coefficient of variation (CV)44.53661421
Kurtosis-0.0891145893
Mean0.02215268596
Median Absolute Deviation (MAD)0.6997992518
Skewness-0.06067713739
Sum22.15268596
Variance0.9733906655
MonotocityNot monotonic
2021-02-12T12:57:02.009060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2709468331
 
0.1%
-1.1306291291
 
0.1%
1.0816893811
 
0.1%
0.64098442891
 
0.1%
-1.1599542251
 
0.1%
-0.076270381831
 
0.1%
1.6439248421
 
0.1%
-0.50238581611
 
0.1%
-0.57901938851
 
0.1%
0.35385931321
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.1519230511
0.1%
-3.0394754541
0.1%
-2.7374631841
0.1%
-2.6971945451
0.1%
-2.5000872411
0.1%
-2.4062280941
0.1%
-2.3999815921
0.1%
-2.344993951
0.1%
-2.3145241621
0.1%
-2.2296935951
0.1%
ValueCountFrequency (%)
3.0835777471
0.1%
2.9511076141
0.1%
2.7251820541
0.1%
2.6611747491
0.1%
2.5968853411
0.1%
2.5883776921
0.1%
2.4545611151
0.1%
2.431155471
0.1%
2.418678291
0.1%
2.418010521
0.1%

X8
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.04596041333
Minimum-2.798235864
Maximum3.053376859
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:57:10.646091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.798235864
5-th percentile-1.772075826
Q1-0.7102375266
median-0.05680902521
Q30.6256157711
95-th percentile1.508124867
Maximum3.053376859
Range5.851612723
Interquartile range (IQR)1.335853298

Descriptive statistics

Standard deviation0.986815406
Coefficient of variation (CV)-21.47098632
Kurtosis-0.2305024955
Mean-0.04596041333
Median Absolute Deviation (MAD)0.6636613152
Skewness-0.05430784397
Sum-45.96041333
Variance0.9738046454
MonotocityNot monotonic
2021-02-12T12:57:19.517007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8939781261
 
0.1%
-0.60796591861
 
0.1%
0.89327599251
 
0.1%
1.4865106251
 
0.1%
0.31798357091
 
0.1%
-0.91227292381
 
0.1%
0.074839458911
 
0.1%
-1.4141434511
 
0.1%
-1.4893177291
 
0.1%
-0.54742017871
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.7982358641
0.1%
-2.7536800451
0.1%
-2.6490887131
0.1%
-2.6138861031
0.1%
-2.5936042151
0.1%
-2.5046299451
0.1%
-2.4702388781
0.1%
-2.418148121
0.1%
-2.3528998381
0.1%
-2.2991535611
0.1%
ValueCountFrequency (%)
3.0533768591
0.1%
2.4924682251
0.1%
2.4778119861
0.1%
2.4360480991
0.1%
2.3010308461
0.1%
2.2815501121
0.1%
2.2515413341
0.1%
2.2309345221
0.1%
2.2126275471
0.1%
2.1335694651
0.1%

X9
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01832163013
Minimum-3.496359382
Maximum3.685609854
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:57:28.021515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.496359382
5-th percentile-1.680658494
Q1-0.6977955528
median-0.03235985236
Q30.653784291
95-th percentile1.617778877
Maximum3.685609854
Range7.181969236
Interquartile range (IQR)1.351579844

Descriptive statistics

Standard deviation1.004188896
Coefficient of variation (CV)-54.80892743
Kurtosis-0.03802412479
Mean-0.01832163013
Median Absolute Deviation (MAD)0.6814208724
Skewness0.08825805365
Sum-18.32163013
Variance1.008395339
MonotocityNot monotonic
2021-02-12T12:57:38.207213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.13747132071
 
0.1%
-0.57934772341
 
0.1%
0.52665297221
 
0.1%
0.7211711331
 
0.1%
-0.36624995511
 
0.1%
0.32806618721
 
0.1%
0.76541015611
 
0.1%
1.5003254351
 
0.1%
0.62660484471
 
0.1%
-0.78706902981
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.4963593821
0.1%
-2.6747943261
0.1%
-2.6555339031
0.1%
-2.4318890531
0.1%
-2.3004222351
0.1%
-2.2803121251
0.1%
-2.2512899811
0.1%
-2.2422920521
0.1%
-2.2385782131
0.1%
-2.2183520171
0.1%
ValueCountFrequency (%)
3.6856098541
0.1%
2.9780692021
0.1%
2.8599022731
0.1%
2.8289143341
0.1%
2.8117456121
0.1%
2.607395161
0.1%
2.4770551891
0.1%
2.4443282261
0.1%
2.4078982241
0.1%
2.37622121
0.1%

target
Categorical

UNIFORM

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
500 
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
0500
50.0%
1500
50.0%
2021-02-12T12:57:55.230306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T12:58:03.813749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring characters

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Interactions

2021-02-12T12:41:53.892164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:42:02.166235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:42:10.318781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:42:18.460096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:42:26.988112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:42:35.759869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:42:44.063469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:42:52.815099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:43:01.551553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:43:10.055040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:43:18.543173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:43:27.003531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:43:36.137889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:43:44.631654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:43:52.958005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:44:01.603293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:44:09.815045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:44:18.034695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:44:26.794389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:44:35.390337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:44:43.570461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:44:51.994126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:45:00.211644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:45:08.782953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:45:16.962154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:45:25.442878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:45:33.595194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:45:41.582562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:45:49.306007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:45:57.242011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:46:05.118727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:46:13.494588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:46:21.122879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:46:28.904386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:46:36.843019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:46:45.302925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:46:54.178681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:47:02.583873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:47:10.907679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:47:19.603929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:47:28.361845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:47:37.414953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:47:45.637871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:47:54.119392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:48:02.262745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:48:10.382174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:48:18.654463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:48:27.014785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:48:35.342053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:48:43.446432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:48:51.695580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:49:00.113185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:49:08.411458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:49:16.461885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:49:25.017979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:49:33.981618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:49:42.761935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:49:51.426762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:50:00.649951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:50:10.269768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:50:20.750355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:50:30.181349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:50:38.662365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:50:47.546631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:50:55.945405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:51:05.460304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:51:14.105574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:51:23.097461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:51:32.370199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:51:40.966561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:51:48.986101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:51:57.242505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:52:06.518942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:52:14.906687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:52:23.727130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:52:32.082850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:52:40.683129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:52:48.866904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:52:57.107911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:53:05.458841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:53:13.675054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:53:21.899405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:53:30.375549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:53:38.727499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:53:47.206937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:53:55.658813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:54:04.419785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:54:12.762348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:54:21.594659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:54:29.902224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-12T12:58:12.315235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-12T12:58:21.590250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-12T12:58:30.398422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-12T12:58:38.660058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-12T12:54:38.188884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-12T12:54:46.720064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

X0X1X2X3X4X5X6X7X8X9target
0-0.4926881.695323-0.463880-0.601031-2.4524460.256386-0.3648100.360809-0.077216-0.9379091
10.3307551.2357861.424151-0.425144-0.366120-0.6088561.090645-0.3562391.830351-1.7468200
20.172737-1.658412-0.885674-0.8153690.4309242.2823470.5188020.3739440.6017362.3615471
3-0.143129-2.2322520.7449140.682386-1.0929251.065166-0.8574871.1233111.491142-1.2537320
42.8520091.1004970.051269-0.4893910.457550-0.2882320.425169-0.2804660.3771160.3779730
51.0698740.360181-0.168978-1.5506712.141164-1.6870780.152103-0.801651-1.030122-0.0470961
60.1075542.500545-1.9002000.077349-1.308384-0.849875-0.947622-0.1133191.9691251.0870741
70.842248-0.086450-0.943249-0.926166-1.643589-0.910122-1.1120870.163783-0.174368-0.0239720
80.7100980.1813502.1455750.7048690.7742690.151737-1.499710-0.174809-0.800607-0.8766170
9-1.6555190.241620-0.476388-0.847240-1.328762-1.834831-1.453458-0.406393-0.9552981.3483951

Last rows

X0X1X2X3X4X5X6X7X8X9target
990-0.1832030.582669-0.1356650.3875430.346412-1.037034-1.006603-1.310142-0.3330830.9879560
9910.8069811.416093-1.4308210.105958-0.7773380.3772350.0445800.7069381.409282-0.2141761
9920.4263190.986449-0.7203902.212073-0.630981-0.315812-0.5126410.796250-1.1829660.6157650
9930.411894-1.2219610.195926-0.4720801.2154181.3204670.110680-0.968358-0.5671650.8378471
994-0.416775-0.058101-0.218471-0.678171-1.171844-0.2661350.510867-1.202038-0.856220-0.5279050
995-0.6700360.862090-0.208514-0.366865-0.834835-1.5632980.263910-0.372874-0.994354-1.0239740
996-0.1689800.856737-0.714346-0.6519870.307338-0.038337-1.3187790.5786881.9036470.1219340
9970.199206-0.2764440.0538571.474468-0.223547-0.0812790.6705200.7255300.0183210.1531191
9980.894915-0.701973-0.030525-0.9500640.1298870.630884-0.6307080.5724130.9863870.3116601
999-0.9849380.0938160.8541840.1493690.497118-0.0110730.5854812.0052050.234621-2.1208690